




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a    :							Dict										=    {
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a    :							Any										=    ['LxmertTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a    :							str										=    [
							    'LxmertEncoder',
							    'LxmertForPreTraining',
							    'LxmertForQuestionAnswering',
							    'LxmertModel',
							    'LxmertPreTrainedModel',
							    'LxmertVisualFeatureEncoder',
							    'LxmertXLayer',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a    :							str										=    [
							    'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFLxmertForPreTraining',
							    'TFLxmertMainLayer',
							    'TFLxmertModel',
							    'TFLxmertPreTrainedModel',
							    'TFLxmertVisualFeatureEncoder',
							]


if TYPE_CHECKING:
							from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
							from .tokenization_lxmert import LxmertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_lxmert_fast import LxmertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_lxmert import (
														    LxmertEncoder,
														    LxmertForPreTraining,
														    LxmertForQuestionAnswering,
														    LxmertModel,
														    LxmertPreTrainedModel,
														    LxmertVisualFeatureEncoder,
														    LxmertXLayer,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_lxmert import (
														    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLxmertForPreTraining,
														    TFLxmertMainLayer,
														    TFLxmertModel,
														    TFLxmertPreTrainedModel,
														    TFLxmertVisualFeatureEncoder,
														)

else:
							import sys

							a    :							str										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






from __future__ import annotations







def  lowerCAmelCase_	(lowerCAmelCase__:		int | str  ):





			"""simple docstring"""


			UpperCAmelCase_:		Optional[int]							      =     str(lowerCAmelCase__  )
			return n == n[::-1]







def  lowerCAmelCase_	(lowerCAmelCase__:		int = 1_0_0_0_0_0_0  ):





			"""simple docstring"""


			UpperCAmelCase_:		int							      =     0

			for i in range(1		,						lowerCAmelCase__  ):
						if is_palindrome(lowerCAmelCase__  ) and is_palindrome(bin(lowerCAmelCase__  ).split("""b"""  )[1]  ):
									total += i
			return total


if __name__ == "__main__":
	print(solution(int(str(input().strip()))))






